53,422 research outputs found
Learning from Synthetic Humans
Estimating human pose, shape, and motion from images and videos are
fundamental challenges with many applications. Recent advances in 2D human pose
estimation use large amounts of manually-labeled training data for learning
convolutional neural networks (CNNs). Such data is time consuming to acquire
and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion
is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL
tasks): a new large-scale dataset with synthetically-generated but realistic
images of people rendered from 3D sequences of human motion capture data. We
generate more than 6 million frames together with ground truth pose, depth
maps, and segmentation masks. We show that CNNs trained on our synthetic
dataset allow for accurate human depth estimation and human part segmentation
in real RGB images. Our results and the new dataset open up new possibilities
for advancing person analysis using cheap and large-scale synthetic data.Comment: Appears in: 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017). 9 page
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
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